The flat joint contact model (FJM) provides significant improvements over its predecessors, the parallel bond and contact bond models, for bonded particle modelling of rocks due to its unique microstructure that allows for the reproduction of the macroscopic compressive–tensile strength ratio, σc/|σt|; internal friction angle, ϕ; and the Hoek–Brown constant, mi. However, the microproperty calibration process is tedious and time-consuming to perform manually due to the various microproperty interdependencies that exist in the FJM. Previous attempts at automating the bonded particle model microproperty calibration process have typically utilised advanced statistical methods, such as artificial neural networks, but they have not yet been widely applied to the FJM over a representative range of confining stresses for calculation of ϕ and mi. In this study, a new method is proposed for automating the FJM microproperty calibration process based on a numerical root-finding algorithm and specific calibration sequencing. The new method is applied to a Rewan Sandstone case study with similar natural porosity to a 2D bonded particle model packed to a low initial mean stress. The resulting FJM microproperties are shown to reproduce both the target macroscopic laboratory properties and a realistic damage evolution, including a normalised crack initiation stress of 0.46 and a normalised crack damage stress of 0.83 coinciding with a reversal of the axial stress–volumetric strain curve in an unconfined compression test simulation. It is also demonstrated that the absolute change in the instantaneous lateral–axial strain ratio (Poisson’s ratio in the linear-elastic phase) provides a reasonable proxy to the acoustic emissions which may be measured in the laboratory.